What is the Model Explainability Tool?

It is a specialized Claude Code skill that helps users understand and interpret the outputs and inner workings of machine learning models through automated analysis.

Can I use this for deep learning models?

Yes, because it uses model-agnostic techniques, this skill can help explain predictions from a wide range of architectures, including neural networks, tree-based models, and ensembles.

How does this help with AI ethics and fairness?

By identifying feature importance, the skill helps detect if a model is relying on biased or protected variables, allowing developers to improve the transparency and accountability of their AI systems.

ML Model Explainability Tool

Name: ML Model Explainability Tool
Author: jeremylongshore

byjeremylongshore

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883

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Data Science & ML

Provides interpretability and transparency for machine learning models by generating explanations for predictions and identifying feature importance.

The ML Model Explainability Tool empowers Claude to act as an interpretability expert, helping users demystify complex 'black box' machine learning models. By leveraging industry-standard techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), this skill analyzes model data to explain the specific reasons behind individual predictions and overall model behavior. It is an essential utility for data scientists and developers looking to debug model performance, ensure fairness, and communicate technical insights to non-technical stakeholders.

Key Features

01Local prediction analysis to explain specific model outputs

02Automated identification of influential feature importance

03Model behavior debugging for identifying unexpected feature interactions

04883 GitHub stars

05Stakeholder-friendly summaries for communicating complex AI insights

06SHAP and LIME integration for robust model-agnostic explanations

Use Cases

01Identifying the key drivers behind customer churn or market trend predictions

02Explaining automated decision outcomes, such as loan rejections or credit approvals

03Auditing models for bias and ensuring transparency in high-stakes AI applications

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills model-explainability-tool

For use in Claude.ai and ChatGPT

Download Skill

Key Features

01Local prediction analysis to explain specific model outputs

02Automated identification of influential feature importance

03Model behavior debugging for identifying unexpected feature interactions

04883 GitHub stars

05Stakeholder-friendly summaries for communicating complex AI insights

06SHAP and LIME integration for robust model-agnostic explanations

Use Cases

01Identifying the key drivers behind customer churn or market trend predictions

02Explaining automated decision outcomes, such as loan rejections or credit approvals

03Auditing models for bias and ensuring transparency in high-stakes AI applications

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills model-explainability-tool

For use in Claude.ai and ChatGPT

Download Skill